Apparatus and methods for estimating time-state physiological parameters

ABSTRACT

A method of determining a value of a physiological parameter for a subject at a selected state includes obtaining, via a device located a distance from the subject, a value of the physiological parameter of the subject at a particular time-of-day, and applying a time-dependent relationship function to the obtained physiological parameter value via a processor to determine a value of the physiological parameter at the selected state.

RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No, 15/219,770, filed Jul. 26, 2016, now U.S. Pat. No.9,521,962, which is a continuation application of U.S. patentapplication Ser. No. 14/116,641,flied Nov. 8, 2013, now U.S. Pat. Ser.No. 9,427,191, which is a 35 U.S.C. §371 national stage application ofPCT Application No. PCT/US2012/046446, filed Jul. 12, 2012, which claimsthe benefit of and priority to U.S. Provisional Patent Application No.61/511,238 filed Jul. 25, 2011, the disclosures of which areincorporated herein by reference as if set forth in their entireties.

FIELD OF THE INVENTION

The present invention relates generally to monitoring apparatus andmethods and, more particularly, to physiological monitoring apparatusand methods.

BACKGROUND OF THE INVENTION

Physiological parameters for living beings are typically a function ofone or more of the following: the time-of-day, environmental conditionsto which a being is exposed, activity level of a being, and variousother physiological parameters. Many of these are related. For example,the average change in heart rate, body temperature, and heart ratevariability (HRV) with the time-of-day are generally known based onhuman and animal studies of the circadian cycle.

SUMMARY

It should be appreciated that this Summary is provided to introduce aselection of concepts in a simplified form, the concepts being furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of thisdisclosure, nor is it intended to limit the scope of the invention.

According to some embodiments of the present invention, a method ofdetermining a value of a physiological parameter for a subject at aselected state (e.g., state of peak metabolism, state of loweredmetabolism, state of rest, etc.), includes obtaining, via a deviceattached to the subject, a value of the physiological parameter of thesubject at a particular time-of-day, and applying a time-dependentrelationship function to the obtained physiological parameter value viaat least one processor to determine a value of the physiologicalparameter at the selected state. Exemplary physiological parametersinclude, but are not limited to, subject body temperature, subject heartrate, subject heart rate variability, subject blood gas levels, subjectmetabolic rate, subject respiration rate, subject blood analyte levels,subject blood pressure, subject pulse pressure, etc. In some embodimentsof the present invention, the time-dependent relationship function isderived from a circadian rhythm for the subject. In some embodiments ofthe present invention, the time-dependent relationship function is alookup table.

In some embodiments of the present invention, determining a value of thephysiological parameter of the subject at a selected state may includeobtaining the value of the physiological parameter at the sametime-of-day for multiple days and determining an average value for themultiple obtained values. Applying the time-dependent relationshipfunction to the obtained physiological parameter value may includeapplying the time-dependent relationship function to the average value.

In some embodiments of the present invention, determining a value of aphysiological parameter for a subject at a selected state may furtherinclude determining if the subject is in a condition of heightenedactivity at the selected time-of-day by determining via the device if atleast one obtained physiological parameter value is at a levelassociated with the heightened activity condition. The time-dependentrelationship function is adjusted for the heightened activity conditionof the subject prior to determining a value of the physiologicalparameter at the selected state.

In some embodiments of the present invention, prior to obtaining a valueof the physiological parameter of the subject at a particulartime-of-day, values of the physiological parameter of the subject areobtained at multiple times during at least one previous day. Apersonalized time-dependent relationship function between at least oneobtained value of the physiological parameter and a value of thephysiological parameter at a time when the subject is at the selectedstate is generated for the subject using the obtained values from the atleast one previous day. Applying the time-dependent relationshipfunction to the obtained physiological parameter value includes applyingthe personalized time-dependent relationship function to the obtainedphysiological parameter value via at least one processor to determine avalue of the physiological parameter at the selected state.

In some embodiments of the present invention, the time-dependentrelationship function is adjusted for calories consumed by the subjectprior to determining a value of the physiological parameter at a timewhen the subject is at the selected state.

In some embodiments of the present invention, the time-dependentrelationship function is adjusted for blood oxygen level of the subjectprior to determining a value of the physiological parameter at a timewhen the subject is at the selected state.

In some embodiments of the present invention, the device includes atleast one physiological sensor that is configured to detect and/ormeasure physiological information from the subject to which the deviceis attached.

In some embodiments of the present invention, the device includes atleast one environmental sensor that detects and/or measuresenvironmental condition information in a vicinity of the subject.Determining a value of a physiological parameter for a subject at aselected state, according to some embodiments of the present invention,further comprises obtaining, via the device, a value of an environmentalparameter in a vicinity of the subject at the particular time-of-day.Applying the time-dependent relationship function to the obtainedphysiological parameter value comprises applying the time-dependentrelationship function and an environmental-dependent relationshipfunction to the obtained physiological parameter value via the at leastone processor to determine a value of the physiological parameter at atime when the subject is at the selected state.

Devices according to embodiments of the present invention may beconfigured to be attached to various portions of the body of a subjectincluding, but not limited to, ear, arm, wrist, leg, hand, foot, finger,toe, chest, head, hair, nose, waist, trunk, shoulder, and neck. Devicesaccording to embodiments of the present invention may also be configuredto be embedded within clothing, foot apparel, and other wearableobjects, without limitation. Additionally, devices according toembodiments of the present invention may be worn outside the body so asto be noninvasive, may be worn inside the body so as to be invasive, ormay be worn subdermally so as to be mildly invasive.

According to other embodiments of the present invention, a method ofdetermining a value of a physiological parameter (e.g., subject bodytemperature, subject heart rate, subject heart rate variability, subjectblood gas levels, subject metabolic rate, subject respiration rate,subject blood analyte levels, subject blood pressure, and subject pulsepressure, etc.) for a subject at rest comprises obtaining, via a deviceattached to the subject, a value of the physiological parameter of thesubject at a particular time-of-day, and applying a time-dependentrelationship function to the obtained physiological parameter value viaat least one processor to determine a value of the physiologicalparameter at a time when the subject is at rest.

In some embodiments of the present invention, determining a value of thephysiological parameter of the subject at rest comprises obtaining thevalue of the physiological parameter at the same time-of-day formultiple days and determining an average value for the multiple obtainedvalues. Applying the time-dependent relationship function to theobtained physiological parameter value may include applying thetime-dependent relationship function to the average value.

In some embodiments of the present invention, determining a value of aphysiological parameter for a subject at rest may further includedetermining if the subject is in a condition of heightened activity atthe time-of-day by determining via the device if at least one obtainedphysiological parameter value is at a level associated with heightenedactivity. The time-dependent relationship function is adjusted for theheightened activity condition of the subject prior to determining avalue of the physiological parameter at a time when the subject is atrest.

In some embodiments of the present invention, prior to obtaining a valueof the physiological parameter of the subject at the time-of-day, valuesof the physiological parameter of the subject are obtained at multipletimes during at least one previous day. A personalized time-dependentrelationship function between at least one obtained value of thephysiological parameter and a value of the physiological parameter at atime when the subject is at rest is generated for the subject using theobtained values from the at least one previous day. Applying thetime-dependent relationship function to the obtained physiologicalparameter value includes applying the personalized time-dependentrelationship function to the obtained physiological parameter value viathe processor to determine a value of the physiological parameter at atime when the subject is at rest.

In some embodiments of the present invention, the time-dependentrelationship function is adjusted for calories consumed by the subjectprior to determining a value of the physiological parameter at a timewhen the subject is at rest.

In some embodiments of the present invention, the time-dependentrelationship function is adjusted for blood oxygen level of the subjectprior to determining a value of the physiological parameter at a timewhen the subject is at rest.

In some embodiments of the present invention, the device includes atleast one environmental sensor that detects and/or measuresenvironmental condition information in a vicinity of the subject.Determining a value of a physiological parameter for a subject at rest,according to some embodiments of the present invention, furthercomprises obtaining, via the device, a value of an environmentalparameter in a vicinity of the subject at a particular time-of-day.Applying the time-dependent relationship function to the obtainedphysiological parameter value comprises applying the time-dependentrelationship function and an environmental-dependent relationshipfunction to the obtained physiological parameter value via at least oneprocessor to determine a value of the physiological parameter at a timewhen the subject is at rest.

According to other embodiments of the present invention, a method ofdetermining a value of a physiological parameter for a subject at aselected state (e.g., state of peak metabolism, state of loweredmetabolism, state of rest, etc.) includes obtaining, via a deviceattached to the subject, a value of the physiological parameter (e.g.,subject body temperature, subject heart rate, subject heart ratevariability, subject blood gas levels, subject metabolic rate, subjectrespiration rate, subject blood analyte levels, subject blood pressure,and subject pulse pressure, etc.) of the subject at a selectedtime-of-day; obtaining, via the device, a value of an environmentalparameter in a vicinity of the subject at the selected time-of-day viathe environmental sensor; determining if the subject is in a conditionof heightened activity at the selected time-of-day by determining viathe device if at least one obtained physiological parameter value is ata level associated with heightened activity; and applying atime-dependent relationship function and an environmental-dependentrelationship function to the obtained physiological parameter value viaa processor to determine a value of the physiological parameter at theselected state, wherein the time-dependent relationship function isadjusted for the heightened activity condition of the subject prior todetermining a value of the physiological parameter at the selectedstate.

In some embodiments of the present invention, obtaining a value of thephysiological parameter of the subject at a particular time-of-dayincludes obtaining the value at the same time-of-day for multiple daysand determining an average value for the multiple obtained values.Applying the time-dependent relationship function and theenvironmental-dependent relationship function to the obtainedphysiological parameter value includes applying the time-dependentrelationship function and the environmental-dependent relationshipfunction to the average value.

In some embodiments of the present invention, prior to obtaining a valueof the physiological parameter of the subject at a particulartime-of-day, values of the physiological parameter of the subject areobtained at multiple times during at least one previous day, and apersonalized time-dependent relationship function between at least oneobtained value of the physiological parameter and a value of thephysiological parameter at the selected state is generated for thesubject using the obtained values from the at least one previous day.Applying the time-dependent relationship function and theenvironmental-dependent relationship function to the obtainedphysiological parameter value includes applying the personalizedtime-dependent relationship function and the environmental-dependentrelationship function to the obtained physiological parameter value viaat least one processor to determine a value of the physiologicalparameter at the selected state.

In some embodiments of the present invention, the time-dependentrelationship function is adjusted for calories consumed by the subjectprior to determining a value of the physiological parameter at theselected state.

In some embodiments of the present invention, the time-dependentrelationship function is adjusted for blood oxygen level of the subjectprior to determining a value of the physiological parameter at theselected state.

According to other embodiments of the present invention, an apparatusincludes a housing configured to be attached to a subject and at leastone physiological sensor attached to the housing, wherein the at leastone physiological sensor detects and/or measures physiologicalinformation from the subject. At least one processor may be attached tothe housing or may be located remotely from the housing. The at leastone processor is in communication with the at least one physiologicalsensor and is configured to obtain from the at least one physiologicalsensor a value of a physiological parameter of the subject at aparticular time-of-day, and to apply a time-dependent relationshipfunction to the obtained physiological parameter value to determine avalue of the physiological parameter at a selected state.

In some embodiments of the present invention, the at least one processoris configured to obtain from the at least one physiological sensor thevalue of the physiological parameter at the same time-of-day formultiple days, determine an average value for the multiple obtainedvalues, and apply the time-dependent relationship function to theaverage value.

In some embodiments of the present invention, the at least one processoris configured to determine if the subject is in a condition ofheightened activity at the time-of-day by determining if at least oneobtained physiological parameter value is at a level associated withheightened activity, and adjust the time-dependent relationship functionfor the heightened activity condition of the subject prior todetermining a value of the physiological parameter at the selectedstate.

In some embodiments of the present invention, the at least one processoris configured to obtain values of the physiological parameter of thesubject at multiple times during at least one previous day, generate apersonalized time-dependent relationship function between at least oneobtained value of the physiological parameter and a value of thephysiological parameter at the selected state using the obtained valuesfrom the at least one previous day, and apply the personalizedtime-dependent relationship function to the obtained physiologicalparameter value to determine a value of the physiological parameter atthe selected state.

In some embodiments of the present invention, the apparatus includes anenvironmental sensor that detects and/or measures environmentalcondition information in a vicinity of the subject. The at least oneprocessor is configured to obtain a value of an environmental parameterin a vicinity of the subject at a particular time-of-day from theenvironmental sensor, and to apply the time-dependent relationshipfunction and an environmental-dependent relationship function to theobtained physiological parameter value to determine a value of thephysiological parameter at the selected state.

In some embodiments of the present invention, the at least one processoris configured to adjust the time-dependent relationship function forcalories consumed by the subject prior to determining a value of thephysiological parameter at the selected state.

In some embodiments of the present invention, the at least one processoris configured to adjust the time-dependent relationship function forblood oxygen level of the subject prior to determining a value of thephysiological parameter at the selected state.

In some embodiments of the present invention, the housing is configuredto be attached to an ear of the subject. In other embodiments, thehousing is configured to be attached to one or more of the followingportions of a body of a subject: arm, wrist, leg, hand, foot, finger,toe, chest, head, hair, nose, waist, trunk, shoulder, and neck. In otherembodiments, the housing is configured to be embedded within clothing,foot apparel, and other wearable objects, without limitation.

Conventional methods and apparatus for studying the circadian cycleheretofore have not estimated resting state (or other) parameters of abeing based on the current state of the being. There have been at leasttwo major limitations preventing such an invention: 1) there has been noeffort to estimate resting parameters based on what's already knownabout the relationships between resting state and current state and 2)there have been no apparatuses or methods for accurately and reliablymeasuring dynamically changing relationships between current and restingstate parameters in everyday life activities.

It is noted that aspects of the invention described with respect to oneembodiment may be incorporated in a different embodiment although notspecifically described relative thereto. That is, all embodiments and/orfeatures of any embodiment can be combined in any way and/orcombination. Applicant reserves the right to change any originally filedclaim or file any new claim accordingly, including the right to be ableto amend any originally filed claim to depend from and/or incorporateany feature of any other claim although not originally claimed in thatmanner. These and other objects and/or aspects of the present inventionare explained in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which form a part of the specification,illustrate various embodiments of the present invention. The drawingsand description together serve to fully explain embodiments of thepresent invention.

FIG. 1 is a block diagram of methods and apparatus for estimatingphysiological parameters of a being at a selected state, according tosome embodiments of the present invention.

FIG. 2 is an exemplary plot of how tympanic or core body temperature maychange with time-of-day for an average person.

FIG. 3 is an exemplary plot of how heart rate may change withtime-of-day for an average person, wherein curve C1 represents a normalcycle and wherein curve C2 represents an elevated cycle.

FIG. 4 is an exemplary plot of how heart rate variability (HRV) maychange with time-of-day for an average person.

FIG. 5 is an exemplary plot of how heart rate of a person may changewith the person's activity level.

FIG. 6 is a flow chart for an algorithm for estimating restingparameters, according to some embodiments of the present invention.

FIG. 7 is a plot of average body temperature vs. time calculated using apolynomial equation for body vs. time-of-day, according to someembodiments of the present invention.

FIG. 8 is a plot of heart rate vs. time for an average person, whereincurve (A) represents heart rate vs. time before a lifestyle change,wherein curve (B) represents heart rate vs. time after a lifestylechange, and wherein curve (A) and (B) are generated via respectivepolynomial equations according to some embodiments of the presentinvention.

FIG. 9 illustrates a functional relationship between an estimatedparameter and multiple time-dependent inputs, according to someembodiments of the present invention.

FIG. 10 is an exemplary lookup table for estimating resting bodytemperature, according to some embodiments of the present invention.

FIG. 11 is a block diagram that illustrates details of an exemplaryprocessor and memory that may be used in accordance with embodiments ofthe present invention.

DETAILED DESCRIPTION

The present invention will now be described more fully hereinafter withreference to the accompanying figures, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein. Like numbers refer to like elementsthroughout. In the figures, certain layers, components or features maybe exaggerated for clarity, and broken lines illustrate optionalfeatures or operations unless specified otherwise. In addition, thesequence of operations (or steps) is not limited to the order presentedin the figures and/or claims unless specifically indicated otherwise.Features described with respect to one figure or embodiment can beassociated with another embodiment or figure although not specificallydescribed or shown as such.

It will be understood that when a feature or element is referred to asbeing “on” another feature or element, it can be directly on the otherfeature or element or intervening features and/or elements may also bepresent. In contrast, when a feature or element is referred to as being“directly on” another feature or element, there are no interveningfeatures or elements present. It will also be understood that, when afeature or element is referred to as being “connected”, “attached” or“coupled” to another feature or element, it can be directly connected,attached or coupled to the other feature or element or interveningfeatures or elements may be present. In contrast, when a feature orelement is referred to as being “directly connected”, “directlyattached” or “directly coupled” to another feature or element, there areno intervening features or elements present. Although described or shownwith respect to one embodiment, the features and elements so describedor shown can apply to other embodiments. It will also be appreciated bythose of skill in the art that references to a structure or feature thatis disposed “adjacent” another feature may have portions that overlap orunderlie the adjacent feature.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, steps, operations, elements, components, and/or groupsthereof. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items.

Spatially relative terms, such as “under”, “below”, “lower”, “over”,“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of over and under. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly”, “downwardly”, “vertical”, “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

It will be understood that although the terms first and second are usedherein to describe various features/elements, these features/elementsshould not be limited by these terms. These terms are only used todistinguish one feature/element from another feature/element. Thus, afirst feature/element discussed below could be termed a secondfeature/element, and similarly, a second feature/element discussed belowcould be termed a first feature/element without departing from theteachings of the present invention.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

The term “housing”, as used herein, may refer to a physical structurefor supporting and/or unifying one or more physical elements of theinvention. For example, the housing of a wearable wrist sensor apparatusor headset sensor apparatus may comprise a structure to support theelectronics, optics, and/or mechanical elements of the sensor. Twospecific, non-limiting examples of a housing may be a “clamshell” of anearbud or one or more PCB boards for the sensor electronics. Thestructure may be composed of plastic, metal, polymer, ceramic, glass,composite material, or virtually any solid stable enough to support thephysical elements of the apparatus.

The term “selected state”, as used herein, includes, but is not limitedto, state of peak metabolism, state of lowered metabolism, state ofrest, a state of one's psychology or mental functioning or the body'sphysiology or physiological functioning. Any example of mentalfunctioning may include psychosocial stress, mental stress, mentalacuity, brain activity, conscious state, state or phase of sleep, or thelike. An example of physiological functioning may include thefunctioning of one or more organs individually or in unison. Inaddition, a selected state may refer to a particular time of day where aparticular or noteworthy mental or physiological event may take place.

The term “heightened activity condition”, as used herein, includes, butis not limited to, elevated heart rate, elevated or lowered vital signsstatus, such as heart status (heart rate, ECG waveform intervals,cardiac output, cardiac stress or load, or the like), lung status(breathing rate, breathing volume, lung stress or load, or the like),blood pressure, blood oxygen level, heart rate variability, galvanicskin response, heat flux from the body, core body temperature, skintemperature, sympathetic or parasympathetic response, or the like.

The term “blood analyte” may refer to blood constituents, such as bloodgases (blood oxygen, blood CO₂, blood hemoglobin, and the like), bloodglucose, blood cholesterol, blood lactic acid, blood bilirubin,dissolved species in the blood, and the like.

The term “headset” includes any type of device or earpiece that may beattached to or near the ear (or ears) of a user and may have variousconfigurations, without limitation. Headsets as described herein mayinclude mono headsets (one earbud) and stereo headsets (two earbuds),earbuds, hearing aids, ear jewelry, face masks, headbands, and the like.

The term “real-time” is used to describe a process of sensing,processing, or transmitting information in a time frame which is equalto or shorter than the minimum timescale at which the information isneeded. For example, the real-time monitoring of pulse rate may resultin a single average pulse-rate measurement every minute, averaged over30 seconds, because an instantaneous pulse rate is often useless to theend user. Typically, averaged physiological and environmentalinformation is more relevant than instantaneous changes. Thus, in thecontext of embodiments of the present invention, signals may sometimesbe processed over several seconds, or even minutes, in order to generatea “real-time” response.

The term “monitoring” refers to the act of measuring, quantifying,qualifying, estimating, sensing, calculating, interpolating,extrapolating, inferring, deducing, or any combination of these actions.More generally, “monitoring” refers to a way of getting information viaone or more sensing elements. For example, “blood health monitoring”includes monitoring blood gas levels, blood hydration, andmetabolite/electrolyte levels.

The term “physiological” refers to matter or energy of or from the bodyof a creature/subject (e.g., humans, animals, etc.). In embodiments ofthe present invention, the term “physiological” is intended to be usedbroadly, covering both physical and psychological matter and energy ofor from the body of a creature. However, in some cases, the term“psychological” is called-out separately to emphasize aspects ofphysiology that are more closely tied to conscious or subconscious brainactivity rather than the activity of other organs, tissues, or cells.Embodiments of the present invention are not limited to use by onlyhumans.

The term “body” refers to the body of a subject (human or animal) whomay wear a headset incorporating embodiments of the present invention.

The terms “being”, “creature”, “subject”, and “organism”, as usedherein, are interchangeable and include, but are not limited to, humansand animals.

The terms “circadian rhythm” and “circadian cycle”, as used herein, areinterchangeable and refer to an endogenously driven, roughly 24-hourcycle in biochemical, physiological, or behavioral processes.

The term “processor” refers to a device that takes one form ofinformation and converts this information into another form, typicallyhaving more usefulness than the original form. For example, in thisinvention, a signal processor may collect raw physiological andenvironmental data from various sensors and process this data into ameaningful assessment, such as pulse rate, blood pressure, or airquality. A variety of microprocessors or other processors may be usedherein. The terms “signal processor”, “processor”, “controller”, and“microcontroller”, as used herein, are interchangeable.

Embodiments of the present invention provide methods and apparatus forestimating time-state physiological parameters/assessments, such asresting parameters, of a subject by factoring functional relationshipsbetween the time-dependent parameters and other measured factors. Forexample, current state parameters (such as vital signs, environmentalexposures, time-of-day, and the like) are measured and a physiologicalmodel is applied to generate an estimation of resting state parameters(or other particular states) of the subject. These relationships can bestatic relationships based on models that apply knowledge of how acurrent state of the subject relates to a resting state of the subject.These relationships can also be dynamic relationships based onpersonalized monitoring of a subject throughout various life activities.

Average body temperature, heart rate, heart rate variability for asubject, as well as many other vital parameters, change throughout theday on a regular schedule dependent on the time-of-day, consistent withnormal circadian rhythms. Additionally, average values of vital signswill change throughout the day based on changes in a subject's physicalactivity or metabolic rate. As shown in FIGS. 2 and 7, average bodytemperature for a human subject may change by ˜2° F. from early morningto mid-day. In FIG. 2, body temperature is plotted on the y-axis andtime in hours is plotted on the x-axis. Curve 100 in FIG. 2 is a plot ofaverage core body temperature vs. time for an average human over anaverage day. In FIG. 7, body temperature is plotted on the y-axis andtime in hours is plotted on the x-axis. Polynomial equation 104 is usedto calculate the plot 102.

Similarly, as shown in FIG. 3, average heart rate for a human subjectmay change by ˜15 BPM (beats per minute) during that same period. FIG. 3illustrates how heart rate may change with time-of-day for an averageperson, wherein curve C1 represents a normal cycle and wherein curve C2represents an elevated cycle. In FIG. 3, heart rate is plotted on they-axis and time in hours is plotted on the x-axis. Thus, if average bodytemperature and average heart rate are being monitored throughout theday, estimating resting body temperature and resting heart rate for asubject may be derived by applying the respective relationships to theexisting measured state.

For example, if the current state at mid-day shows a body temperature of99.6° F. and heart rate of 80 BPM, the resting state values may beestimated as 97.6° F. and 65 BPM, by subtracting 2° F. from bodytemperature and 15 BPM from heart rate, respectively. Percent (%) changerelationships for estimating resting state, as opposed to absolute valuechanges, may also be used to estimate resting state. For example, thechange from current state to resting state in the aforementioned case is˜2% and 18% for body temperature and resting heart rate, respectively.Thus, a lookup table (e.g., table 110, FIG. 10) may be generated withtime-of-day in one column, measured value in a second column, and theassociated percent multiplier in a third column, where the percentmultiplier is the ratio between resting value and measured value. Theestimation for resting value can then be generated by multiplying thistime-of-day-dependent ratio by the measured vital parameter at the giventime-of-day of the measurement.

A specific example for the case of body temperature is presented in FIG.10, using the same relationships and data of FIG. 2, where a measurementof body temperature has been made at 6 PM, with a multiplier of ˜0.981,yielding a resting body temp estimate of 97.6° F. A complimentary lookuptable may replace the % multiplier column with a ±add/subtract column,where the ±number is the amount to add or subtract from the measuredvalue to generate an estimate of the resting value.

It should also be noted that a formula, rather than a lookup table, mayalso be used to relate resting estimates to measured estimates,depending on the time-of-day. For example, a polynomial formularepresenting the table of FIG. 10 may be: Estimated RestingValue=(Measured Value)*(−0.29x⁴+0.79x³−0.63x²+0.13x+1.0), where x=timein hours, starting at x=0 for 7 AM. It is apparent from this formulathat at 7 AM (x=0), the Estimated Resting Value=Measured Value, which isexpected in the aforementioned formalism.

FIG. 7 shows an exemplary plot 102 of average human body temperatureduring a day, along with an approximate functional relationship foraverage body temperature vs. time. In the illustrated embodiment, thefunctional relationship is a polynomial equation 104 of order n=4. Thea₀ term is 97.6° F., representing the resting body temperature, orBT_(resting)=97.6° F. If a person's resting body temperature(BT_(resting)) had started higher or lower than 97.6° F., the polynomialequation may still hold, with the only substantial difference being thea₀ term for BT_(resting). Thus, an equation for estimating BT_(resting)VS. BT_(measured) may be derived by rearranging terms such thata₀=BT_(resting)=BT_(measured)−[9E−05(t⁴)−0.0033(t³)+0.0161(t²)+0.2868(t)].More generally,a₀=BT_(resting)=BT_(measured)−[a_(n)(t^(n))+a_(n−1)(t^(n−1))+a_(n−2)(t^(n−2))+. . . a₁(t)], where “n” is the order of the polynomial equation andwhere t=0 represents the value of BT at the resting state. The secondterm in the brackets may be related to body temperature changes due tocircadian rhythms, such that BT_(resting)=BT_(measured)−ΔBT_(circadian). This same general relationship may be applied towardsheart rate (HR) as shown in FIG. 3 and heart rate variability in FIG. 4.In FIG. 4, heart rate variability is plotted on the y-axis and time inhours is plotted on the x-axis. The units along the y-axis aremilliseconds-squared. Plot 120 illustrates how heart rate variability(HRV) may change with time-of-day for an average person. For example,HR_(resting)=HR_(measured)−Δ HR_(circadian) andHRV_(resting)=HRV_(measured)−Δ HRV_(circadian). This formalism may beapplied towards blood oxygen (SPO₂), respiration rate (RR), bloodpressure (BP), pulse pressure (PP), and other vital parameters (VP),that may change regularly throughout the day according toVP_(resting)=VP_(measured)−Δ VP_(circadian).

In some embodiments of the present invention, the general relationshipsillustrated in FIGS. 2, 3, and 7 may not enable a prediction of restingstate (or other selected state) as accurately as desired. For example, aperson engaging in heightened activity or personal variations may causesubstantial departures from the general relationships. Fortunately,these differences may effectively average out if vital signsmeasurements are made over a period of time. For example, if a vitalsign of interest is measured over several days at the same time-of-day,during different activity levels, the average value of the vital signmay be largely divorced of convolutions caused by heightened or acuteactivity. This average value of VP_(measured) may then be used toestimate VP_(resting). However, it may be difficult to measure the vitalparameter at the same time-of-day for multiple days, and a one-timemeasurement of a vital parameter taken during or following high activitymay not be sufficient to support an accurate estimate of the restingvalue, using the general relationships alone.

To correct for conditions of high activity, a general relationshipbetween changes in activity and changes in vital parameters may be usedto correct an estimate of resting parameters, according to someembodiments of the present invention. For example, during activity, theheart rate of a subject may increase in a predictable fashion, as shownin FIG. 5. In FIG. 5, heart rate is plotted on the left y-axis, activityis plotted on the right y-axis, and time in hours is plotted on thex-axis. Curve A in FIG. 5 illustrates heart rate and activity for aperson at a first state, and curve B in FIG. 5 illustrates heart rateand activity for the person at a heightened activity state.

Generally, as illustrated in FIG. 5, heart rate will increase withactivity, and the rate of increase with activity may be proportional tothe physical fitness or aerobic capacity (VO₂max) of the subject. Thus,if the relationship between heart rate and activity is known for asubject, then the differential increase in heart rate due to elevatedactivity, for a particular time-of-day, may be subtracted from thecurrent heart rate measurement. Namely, the estimated resting heart ratecan be defined as HR_(resting)=HR_(measured)−Δ HR_(circadian)−ΔHR_(activity), where Δ HR_(activity) is the change in heart rate withactivity (either positive/negative for increases/decreases in heart ratewith activity). For other vital parameters, the term Δ VP_(activity) maybe used instead.

Personal differences between individuals may cause deviations from thegeneral, “universal” relationships previously described. For example,subjects having a higher metabolic rate change throughout a given daymay have a larger total value or percent change in vital parametersbetween early morning and midday. Improving the accuracy of estimatingresting parameters can be achieved by measuring vital parametersthroughout the day, generating average time-dependent relationshipsbased on these measured parameters, and deriving a personalizedtime-dependent relationship between measured and resting parameters.Once a personalized, functional, time-dependent relationship isgenerated between measured (current state) vital parameters and restingstate vital parameters, this model may be used to estimate resting stateparameters by inputting current state measurements into the model. Forcurrent state measurements taken at high activity, stored relationshipsbetween a subject's vital parameters and activity can be employed tosubtract the change in vital parameter values with activity (ΔVP_(activity)) as with the heart rate example described above.

Environmental exposures (i.e., environmental conditions to which asubject is exposed) may also affect the time-dependent relationshipbetween measured and resting parameters. For example, sunlight exposuremay elevate metabolism, resulting in an increase in heart rate,breathing, rate, other vital parameters, or the like. In such case, itmay be insufficient to estimate resting parameters by simply measuringcurrent resting parameters and inputting current parameters into thefunctional model. Rather, it may be more accurate to estimate restingvital parameters according to VP_(resting)=VP_(measured)−ΔVP_(circadian)−Δ VP_(environmental exposure) where ΔVP_(environmental exposure) is the change (positive or negative) in thevital parameter value due to environmental exposure (e.g., sunlightexposure, etc.). A change in vital parameters may be caused by manydifferent forms of environmental exposures such as, but not limited to,loud noises, strong wind, extreme temperatures, short wavelength light(e.g., light at wavelengths <470 nm), airborne pollution, mechanicalstress, and the like.

It should be noted that multiple parameters may simultaneously affectthe relationship between resting values and measured values. Forexample, if physical activity and environmental exposure both have animpact on instantaneous vital parameters, then a more generalrelationship for resting vital parameters may be:VP_(resting)=VP_(measured)−Δ VP_(circadian)−Δ VP_(activity)−ΔVP_(environmental exposure). Additional relationships, such as changesin a vital parameter with food intake (calories consumed), blood oxygen(SPO₂), and the like may also be incorporated. These additionalrelationships can be accommodated by Δ VPn, where the integer “n”represents an additional factor which may affect VP_(measured).

The aforementioned models for estimating resting parameters have beenpresented as “static” models. A static model, once implemented orderived, stays fixed with time. However, models for determining a valueof a physiological parameter for a subject at a selected state (e.g., atrest, etc.), according to some embodiments of the present invention, mayalso be dynamic, changing with time based on updated information about aparticular person or group of people. For example, a person undergoingcardiac therapy, a new diet, drug therapy, or other lifestyle change maysee an acute or chronic change in metabolism over time. In such case,static models may be insufficient for estimating resting parameters fromcurrent state measured parameters. Rather, it may be more accurate tomeasure vital parameters over a period of time and update the modelbased on updated relationships between resting and current stateparameters.

FIG. 8 shows a specific example of a dynamic model for determining avalue of a physiological parameter for a subject at a selected state(e.g., at rest, etc.) according to some embodiments of the presentinvention. FIG. 8 is a graph of heart rate vs. time-of-day for atwenty-four hour (24 hr) period. Curves A and B in FIG. 8 show thecircadian change in average heart rate profile of an average personbefore and after a lifestyle change. Polynomial representations of theseplots are also shown, with a polynomial order of n=5. Polynomialequation 130 is used to generate curve A in FIG. 8 and polynomialequation 140 is used to generate curve B in FIG. 8.

As illustrated in FIG. 8, after the lifestyle change, the average heartrate changes more dramatically as the time-of-day changes, asexemplified by the 2× increase in the 5th-power coefficient in the “B”curve when compared with the “A” curve. The resting heart rate for eachcase is the a₀ terms of each, 60 and 45 BPM for curves A and B,respectively. Because the change in each model (for curves A and B) isnot merely a scalar addition or subtraction, accurately estimatingresting heart rate may require measuring average heart rate throughoutthe day, over the course of several days, modeling a relationshipbetween resting and current state parameters, and then inputting currentstate parameters into the model. In one embodiment of the presentinvention, the model may be a polynomial equation fit to the measureddata, as shown in FIG. 8. However, other models may be employed toimprove accuracy or model simplicity, according to embodiments of thepresent invention.

FIG. 1 is a block diagram of methods and apparatus for generatingestimated physiological parameters of a subject at a selected statebased on current state measurements, according to some embodiments ofthe present invention. Time-correlated physiological metrics (Block 10)and/or time-correlated environmental metrics (Block 20) are collectedand are input into a physiological model (static, dynamic, or acombination of both) (Block 30), and the desired parameters (such asresting state parameters) are estimated (Block 40) based on the metricsand physiological model. The estimated parameters are then reported inan organized fashion (Block 50).

An algorithm for estimating a resting parameter according to embodimentsof the present invention is presented in the flow chart of FIG. 6. Thealgorithm includes measuring time-correlated heart rate (HR),respiration rate (RR), activity levels, etc., at a particular time ofday (Block 150). A physical model is applied to estimate a parametervalue that accounts for changes in the time-correlated parameter withactivity, environmental exposure, and/or daily cycle (Block 152). Aresting assessment is then generated (Block 154).

FIG. 11 illustrates an exemplary processor 200 and memory 202 that maybe used in a wearable device and/or a device remote from a subject tocarry out various embodiments of the present invention. The processor200 communicates with the memory 202 via an address/data bus 204. Theprocessor 200 may be, for example, a commercially available or custommicroprocessor or similar data processing device. The memory 202 isrepresentative of the overall hierarchy of memory devices containing thesoftware and data used to perform the various operations describedherein. The memory 202 may include, but is not limited to, the followingtypes of devices: cache, ROM, PROM, EPROM, EEPROM, flash, SRAM, andDRAM.

As shown in FIG. 11, the memory 202 may hold various categories ofsoftware and data: an operating system 206, a physiological datacollection module 208, an environmental data collection module 210, anda time-dependent relationship function module 212. The operating system206 controls operations of the physiological and environmental sensors.

It should be noted that estimating a physiological metric at a selectedtime state need not necessarily require a direct measurement of currenttime. For example, if the relationship between at least onephysiological parameter and time is already known, or if thatrelationship is developed by recording data for a subject wearing aphysiological sensor over time as shown in FIG. 11, then the currenttime may be inferred through this relationship and factored into anestimate for a second physiological metric at a selected time state. Forexample, if the relationship between the subject's core body temperatureover time is known (as shown in FIG. 2), then a subject's HRV may beestimated for a selected time-state by estimating the current time basedon the measured core body temperature at a current state. In such case,the current time need not be measured. However, in such case, becausethe current time is inferred and not directly noted or measured, it maybe further beneficial to measure the subject's activity and have thisfactored into the relationship function 212 (as discussed for FIG. 5) soas to prevent activity-based artifacts from causing inaccurate estimatesof a second physiological metric at a selected state.

The physiological data collection module 208 comprises logic forobtaining from a physiological sensor a value of a physiologicalparameter of a subject at a particular time-of-day. The physiologicaldata collection module 208 may also comprise logic for obtaining from aphysiological sensor the value of a physiological parameter at the sametime-of-day for multiple days, and logic for determining an averagevalue for the multiple obtained values. The physiological datacollection module 208 may also comprise logic for obtaining values of aphysiological parameter of a subject at multiple times during at leastone previous day.

The environmental data collection module 210 comprises logic forobtaining from an environmental sensor a value of an environmentalparameter in a vicinity of a subject at a particular time-of-day.

The time-dependent relationship function module 212 comprises logic forapplying a time-dependent relationship function to an obtainedphysiological parameter value to determine a value of the physiologicalparameter at a selected state, and logic for applying the time-dependentrelationship function and an environmental-dependent relationshipfunction to an obtained physiological parameter value (and averagevalue) to determine a value of the physiological parameter at theselected state. In addition, the time-dependent relationship functionmodule 212 may comprise logic for determining if a subject is in acondition of heightened activity at a selected time-of-day bydetermining if at least one obtained physiological parameter value is ata level associated with heightened activity, and may comprise logic foradjusting the time-dependent relationship function for the heightenedactivity condition of the subject prior to determining a value of thephysiological parameter at the selected state. The time-dependentrelationship function module 212 may comprise logic for generating apersonalized time-dependent relationship function between a measuredvalue of a physiological value and a value of the physiologicalparameter at a selected state using obtained values from at least oneprevious day, and may comprise logic for applying the personalizedtime-dependent relationship function to a obtained physiologicalparameter value to determine a value of the physiological parameter atthe selected state.

The time-dependent relationship function module 212 may also compriselogic for adjusting a time-dependent relationship function for caloriesconsumed by a subject prior to determining a value of the physiologicalparameter at a selected state. The time-dependent relationship functionmodule 212 may also comprise logic for adjusting a time-dependentrelationship function for blood oxygen level of a subject prior todetermining a value of the physiological parameter at a selected state.

According to some embodiments of the present invention, collectingmeasured time-dependent metrics may be manual or automatic. Records canbe taken over time, recorded, and processed into time-dependentrelationships by skilled professionals or personal recording. However,it may be easier to record this data with one or more wearable deviceshaving multiple wearable sensors. For example, wired and wireless vitalparameter modules may be located along several parts of the body, orintegrated into a single device worn at a single place along the body.These wearable devices may measure vital parameters throughout the dayand, with microprocessors or other processing devices, generateestimations for resting parameters. Memory devices, such as memorychips, data storage devices, and the like, may be used to store andupdate physiological models according to embodiments of the presentinvention, and at least one processor may be used to estimate parametersbased on the metrics and model. At least one processor may also be usedto organize the data into a string of outputs for each measuredparameter. There is great flexibility in the electronics that may beused to implement embodiments of the present invention. Individualelectronic components or chips may be used and integrated within circuitboard, or the electronics may integrate memory storage, data processing,and data translation within a single chip, or other combinations orelectronics configurations may be used.

Some types of wearable devices may be more suited for sensor integrationthan other devices. For example, an ear-worn device may be especiallysuited for measuring blood flow, heart rate, breathing rate, EEG, andbody temperature, due in part to the location of the ear with respect tophysiological structures such as the carotid artery, capillaries, earblood vessels, the brain, and the tympanic membrane. However many otherform-factors for a single wearable device may be employed. For example,strong blood flow and heat generation in the limbs, digits, and torsoenable integrated sensor locations in the arms, wrist, legs, hands,feet, fingers, toes, chest, head, hair, nose, waist, trunk, shoulder,neck, and other locations. Furthermore, physiological and environmentalsensors may be embedded in clothing or other wearable devices, such asheadsets, earbuds, wrist watches, adhesive patches, rings, bracelets,necklaces, footwear, socks, shirts, pants, underwear, earrings and otherbody piercings, hats, glasses, and the like.

The ear is an ideal location for wearable health and environmentalmonitors. The ear is a relatively immobile platform that does notobstruct a person's movement or vision. Headsets located at an ear have,for example, access to the inner-ear canal and tympanic membrane (formeasuring core body temperature), muscle tissue (for monitoring muscletension), the pinna and earlobe (for monitoring blood gas levels), theregion behind the ear (for measuring skin temperature and galvanic skinresponse), and the internal carotid artery (for measuringcardiopulmonary functioning), etc. The ear is also at or near the pointof exposure to: environmental breathable toxicants of interest (volatileorganic compounds, pollution, etc.); noise pollution experienced by theear; and lighting conditions for the eye. Furthermore, as the ear canalis naturally designed for transmitting acoustical energy, the earprovides a good location for monitoring internal sounds, such asheartbeat, breathing rate, and mouth motion.

Wireless, Bluetooth®-enabled, and/or other personal communicationheadsets may be configured to incorporate physiological and/orenvironmental sensors, according to some embodiments of the presentinvention. As a specific example, Bluetooth® headsets are typicallylightweight, unobtrusive devices that have become widely acceptedsocially. Moreover, Bluetooth® headsets are cost effective, easy to use,and are often worn by users for most of their waking hours whileattending or waiting for cell phone calls. Bluetooth® headsetsconfigured according to embodiments of the present invention areadvantageous because they provide a function for the user beyond healthmonitoring, such as personal communication and multimedia applications,thereby encouraging user compliance. Exemplary physiological andenvironmental sensors that may be incorporated into a Bluetooth® orother type of headsets include, but are not limited to accelerometers,auscultatory sensors, pressure sensors, humidity sensors, color sensors,light intensity sensors, pressure sensors, etc.

Optical coupling into the blood vessels of the ear may vary betweenindividuals. As used herein, the term “coupling” refers to theinteraction or communication between excitation light entering a regionand the region itself. For example, one form of optical coupling may bethe interaction between excitation light generated from within alight-guiding earbud and the blood vessels of the ear. Light guidingearbuds are described in co-pending U.S. Patent Application PublicationNo. 2010/0217102, which is incorporated herein by reference in itsentirety. In one embodiment, this interaction may involve excitationlight entering the ear region and scattering from a blood vessel in theear such that the intensity of scattered light is proportional to bloodflow within the blood vessel. Another form of optical coupling may bethe interaction between excitation light generated by an optical emitterwithin an earbud and the light-guiding region of the earbud.

Embodiments of the present invention are not limited to headsets anddevices that communicate wirelessly. In some embodiments of the presentinvention, headsets and devices configured to monitor an individual'sphysiology and/or environment may be wired to a device that storesand/or processes data. In some embodiments, this information may bestored on the headset itself.

Embodiments of the present invention may apply to the estimation ofparameters at another state (other than resting state) by measuringcurrent state parameters and processing this data via a model togenerate an estimate of parameters at the desired state. For example,embodiments of the present invention may be applied to estimating vitalparameters at the state of peak metabolism during midday. In anotherexample, embodiments of the present invention may be applied toestimating vital parameters at the state of lowered metabolismassociated with evening time, for example, just before bedtime. A moregeneral model or equation 160 for estimating vital parameters at adesired state is presented in FIG. 9.

It should also be understood that user input may be used to improve theaccuracy of resting state (or other state) parameters. For example,relationships may exist between resting vs. measured parameters andweight, gender, height, habitual information, and the like. A particularexample of habitual information may be the time-of-day someone wakes upin the morning. For example, the time of waking up may replace “7 AM”shown in FIG. 2.

Methods for estimating a physiological parameter at a selected state,according to some embodiments of the present invention, may be appliedto wearable sensors as well as nonwearable sensors capable of measuringone or more physiological parameters and physical activity in atime-correlated manner, during enough times of the day to build orexecute a time-correlated model. For example, whereas multiple examplesof wearable devices have been described herein, alternative embodimentsmay employ wall-mounted sensors, bed-mounted sensors, car-mountedsensors, portable sensors, or other sensor configurations that canmeasure physiological parameters or physical activity at a “stand-off”distance from a subject. As a specific example, a wall-mounted cameramay be configured to measure the heart rate, breathing rate, andphysical activity of a subject and to record a time stamp of thatsubject. Measuring heart rate or breathing rate with a mounted cameramay be achieved via algorithms capable of assessing individual videoframes for changes in the chest size in time or by detecting certainwavelengths of light associated with heat changes, for example.Additionally, physical activity may be assessed by algorithms capable ofidentifying subjects and subject motion and translating this identifiedmotion to activity level. In such case, a time-correlated relationshipfunction may be generated for the subject. Thus, the heart rate orrespiration rate of the subject may be accurately estimated for aselected state, such as a resting state or other state, when the subjectis not in view of the camera by applying the time-correlatedrelationship function for the subject at the selected state. Suitablestand-off detection methods may employ, for example, electromagnetic,electrical, magnetic, inductive, capacitive, thermal, acoustic, or otherenergy detection techniques. For example, the heart rate, breathingrate, and activity of a subject sleeping in a bed, and coupled to astand-off capacitive or inductive sensor, may be monitored throughchanges in capacitance or inductance. If this data is time-stamped by aprocessor, the inventive aspects described herein may be applied fordetermining one or more physiological parameters for that subject at aselected state, such as a state of reduced or elevated activity.

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. The invention is defined by the following claims, withequivalents of the claims to be included therein.

That which is claimed is:
 1. A method of estimating a value of aphysiological parameter for a subject at a selected metabolic state, themethod comprising: obtaining, via a non-wearable device located at astand-off distance from the subject, a value of the physiologicalparameter of the subiect at a time-of-day, wherein the non-wearabledevice comprises a sensor configured to measure heart rate, breathingrate, and/or physical activity of the subject, wherein the sensor is anelectromagnetic energy sensor, an electrical energy sensor, a magneticenergy sensor, an inductive energy sensor, a capacitive energy sensor, athermal energy sensor, or an acoustic energy sensor; and estimating thevalue of the physiological parameter for the subject at the selectedmetabolic state by applying a time-dependent relationship function tothe obtained physiological parameter value via at least one processor,wherein the selected metabolic state is different from a currentmetabolic state of the subject.
 2. The method of claim 1, wherein thetime-dependent relationship function is derived, from a circadian rhythmfor the subject.
 3. The method of claim 1, wherein the time-dependentrelationship function is a lookup table.
 4. The method of claim 1,wherein the physiological parameter includes one or more of thefollowing: subject body temperature, subject heart rate, subject heartrate variability, subject blood gas levels, subject metabolic rate,subject respiration rate, subject blood analyte levels, subject bloodpressure, and subject pulse pressure.
 5. The method of claim 1, whereinestimating the value of the physiological parameter for the subject atthe selected metabolic state comprises obtaining the value of thephysiological parameter at the same time-of-day for multiple days anddetermining an average value for the multiple obtained values, andwherein applying the time-dependent relationship function to theobtained physiological parameter value comprises applying thetime-dependent relationship function to the average value.
 6. The methodof claim 1, further comprising determining if the subject is in acondition of heightened activity at the time-of-day by determining viathe non-wearable device if at least one obtained physiological parametervalue is at a level associated with heightened activity, and wherein thetime-dependent relationship function is adjusted for the heightenedactivity condition of the subject prior to estimating the value of thephysiological parameter at the selected metabolic state.
 7. The methodof claim 1, wherein prior to obtaining a value of the physiologicalparameter of the subject at the time-of-day, values of the physiologicalparameter of the subject are obtained at multiple times during at leastone previous day, and a personalized time-dependent relationshipfunction between at least one of value of the physiological parameterand a value of the physiological parameter at the selected metabolicstate is generated for the subject using the obtained values from the atleast one previous day, and wherein applying the time-dependentrelationship function to the obtained physiological parameter valuecomprises applying the personalized time-dependent relationship functionto the obtained physiological parameter value via the at least oneprocessor to estimate the value of the physiological parameter at theselected metabolic state.
 8. The method of claim 1, wherein thenon-wearable device further includes an environmental sensor thatdetects and/or measures environmental condition information in avicinity of the subject, and further comprising obtaining, via thenon-wearable device, a value of an environmental parameter in a vicinityof the subject at the time-of-day, and wherein applying thetime-dependent relationship function to the obtained physiologicalparameter value comprises applying the time-dependent relationshipfunction and an environmental-dependent relationship function to theobtained physiological parameter value via the at least one processor toestimate the value of the physiological parameter at the selectedmetabolic state.
 9. The method of claim 1, further comprising adjustingthe time-dependent relationship function for one or more of thefollowing prior to estimating the value of the physiological parameterat the selected metabolic state: calories consumed by the subject andblood oxygen level of the subject.
 10. The method of claim 1, whereinthe selected metabolic state includes one or more of the following:state of peak metabolism, state of lowered metabolism, state of rest.11. The method of claim 1, wherein the non-wearable device is awall-mounted device, a bed-mounted device, a vehicle-mounted device, ora portable device.
 12. The method of claim 1, wherein the non-wearabledevice is configured to identify subject motion and to translateidentified subject motion to subject activity level.
 13. A system,comprising: a non-wearable sensor configured to measure heart rate,breathing rate, and/or physical activity of a subject located at astand-off distance from the non-wearable sensor, wherein thenon-wearable sensor is an electromagnetic energy sensor, an electricalenergy sensor, a magnetic energy sensor, an inductive energy sensor, acapacitive energy sensor, a thermal energy sensor, or an acoustic energysensor; and at least one processor configured to obtain from thenon-wearable sensor a value of a physiological parameter of the subjectat a time-of-day, and to estimate a value of the physiological parameterfor the subject at a selected metabolic state by applying atime-dependent relationship function to the obtained physiologicalparameter value, wherein the selected metabolic state is different froma current metabolic state of the subject.
 14. The system of claim 13,wherein the at least one processor is configured to: obtain from thenon-wearable sensor the value of the physiological parameter at the sametime-of-day for multiple days; determine an average value for themultiple obtained values; and apply the time-dependent, relationshipfunction to the average value.
 15. The system of claim 13, wherein theat least one processor is configured to: determine if the subject is ina condition of heightened activity at the selected time-of-day bydetermining if a heart rate of the subject is at an elevated level; andadjust the time-dependent relationship function for the elevated heartrate level of the subject prior to estimating the value of thephysiological parameter at the selected metabolic state.
 16. The systemof claim 13, wherein the at least one processor is configured to: obtainvalues of the physiological parameter of the subject at multiple timesduring at least one previous day; generate a personalized time-dependentrelationship function between a measured value of the physiologicalvalue and the estimated value of the physiological parameter at theselected metabolic state using the obtained values from the at least oneprevious day; and estimate the value of the physiological parameter forthe subject at the selected metabolic state by applying the personalizedtime-dependent relationship function to the obtained physiologicalparameter value.
 17. The system of claim 13, further comprising anon-wearable environmental sensor that detects and/or measuresenvironmental condition information in a vicinity of the subject, andwherein the at least one processor is configured to obtain a value of anenvironmental parameter in a vicinity of the subject at the time-of-dayfrom the non-wearable environmental sensor, and to apply thetime-dependent relationship function and an environmental-dependentrelationship function to the obtained physiological parameter value toestimate the value of the physiological parameter at the selectedmetabolic state.
 18. The system of claim 13, wherein the at least oneprocessor is configured to adjust the time-dependent relationshipfunction for one or more of the following prior to estimating the valueof the physiological parameter at the selected metabolic state: caloriesconsumed by the subject and blood oxygen level of the subject.
 19. Thesystem of claim 13, wherein the at least one processor is configured toadjust the time-dependent relationship function for blood oxygen levelof the subject prior to estimating the value of the physiologicalparameter at the selected metabolic state.
 20. The system of claim 13,wherein the non-wearable sensor is configured to be wall-mounted,bed-mounted, vehicle-mounted, or portable.
 21. The system of claim 13,wherein the non-wearable sensor is configured to identify subject motionand to translate identified subject motion to subject activity level.